Legal Information Retrieval with Generalized Language Models
By Julien Rossi, Evangelos Kanoulas. 2019
Subfields: Information Retrieval & Search, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Retrieval & Search
- Jimmy J. Lin
- Rodrigo Nogueira
- Andrew Yates
- Zeynep Akkalyoncu Yilmaz
- Mi-Young Kim
- Juliano Rabelo
- Randy Goebel
- Sebastian Hofstätter
In Natural Language Processing
- Jimmy J. Lin
- Rodrigo Nogueira
- Andrew Yates
- Ken Satoh
- Mi-Young Kim
- Juliano Rabelo
- Masaharu Yoshioka
- Yoshinobu Kano
Citations in the corpus
- Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “Among the seven participating teams for task 1, the submissions of the top two teams were based on neural networks and the second best used a BERT-based reranker similar to monoBERT [Rossi and Kanoulas, 2019].”
- A Summary of the COLIEE 2019 Competition (2019, methodology): “– ILPS (three runs) [21] combines text summarizing and a generalized language model (BERT) in order to assess pairwise relevance.”